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[Dist][Inference] Further TP fix to make sure e2e TP is working #878

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Jul 3, 2024
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[Dist][Inference] Further TP fix to make sure e2e TP is working #878

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39 changes: 16 additions & 23 deletions distributed/parallelize_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,55 +44,48 @@ def apply_tp(

tp_mesh = world_mesh["tp"]

# TODO: To figure out the TP for the tok_embedding and the linear proj layer.
# # 1. Parallelize the first embedding and the last linear proj layer
# # 2. Shard the first transformer block's inputs
# TODO: The commented part can further help with scaling but it will
# make inference very slow so we disable it for now.
# Parallelize the token embedding and the last linear proj layer
# model = parallelize_module(
# model,
# tp_mesh,
# {
# "tok_embeddings": RowwiseParallel(
# input_layouts=Replicate(),
# "tok_embeddings": ColwiseParallel(
# output_layouts=Replicate(),
# ),
# "output": ColwiseParallel(
# input_layouts=Shard(1),
# output_layouts=Replicate(),
# use_local_output=True,
# ),
# },
# )

# NOTE: This is indeed a hack because it assumes that we create cache
# after we apply TP to the model. Because we don't want to change model code
# when applying TP. We need to have change to ensure KVCache has the correct
# size as k and v.
model.config.n_local_heads = model.config.n_local_heads // tp_mesh.size()

# Apply tensor parallelism to every transformer block
for transformer_block in model.layers:
layer_plan = {
"attention": PrepareModuleInput(
input_layouts=(Replicate(), None),
desired_input_layouts=(Replicate(), None),
),
"attention.wq": ColwiseParallel(),
"attention.wk": ColwiseParallel(),
"attention.wv": ColwiseParallel(),
"attention.wo": RowwiseParallel(
output_layouts=Replicate(),
use_local_output=True,
),
"feed_forward": PrepareModuleInput(
input_layouts=(Replicate(),),
desired_input_layouts=(Replicate(),),
),
"attention.wo": RowwiseParallel(),
"feed_forward.w1": ColwiseParallel(),
"feed_forward.w2": RowwiseParallel(
output_layouts=Replicate(),
use_local_output=True
),
"feed_forward.w2": RowwiseParallel(),
"feed_forward.w3": ColwiseParallel(),
}

# Adjust attention module to use the local number of heads
attn_layer = transformer_block.attention
assert attn_layer.n_heads % tp_mesh.size() == 0
assert attn_layer.n_local_heads % tp_mesh.size() == 0
assert attn_layer.dim % tp_mesh.size() == 0
attn_layer.n_heads = attn_layer.n_heads // tp_mesh.size()
attn_layer.n_local_heads = attn_layer.n_local_heads // tp_mesh.size()
attn_layer.dim = attn_layer.dim // tp_mesh.size()

parallelize_module(
module=transformer_block,
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